Adquisición y Procesamiento de Señales Biomédicas en Tecnologías de Borde

Ingeniería Biomédica

Ph.D. Pablo Eduardo Caicedo Rodríguez

2025-01-20

Adquisición y Procesamiento de Señales Biomédicas en Tecnologías de Borde - APSB

Methodology for designing an edge ai device

  1. Problem Definition & Use Case Analysis
  2. Data Collection & Preprocessing
  3. Model Selection & Optimization
  4. Hardware Selection
  5. Deployment & Model Inference
  6. Testing, Validation, and Continuous Improvement
  7. Final Deployment & Scaling

Problem Definition & Use Case Analysis

  • Identify the specific AI task (e.g., real-time ECG analysis, fall detection, predictive maintenance in IoT).
  • Determine operational constraints, including:
    • Power consumption (battery-operated vs. wired).
    • Latency requirements (real-time processing vs. periodic updates).
    • Communication needs (Wi-Fi, Bluetooth, LoRa, standalone processing).

Data Collection & Preprocessing

  • Sensor Selection: Choose sensors relevant to the application (e.g., accelerometers for motion tracking, biosensors for health monitoring).
  • Edge-Compatible Data Acquisition: Optimize data formats to reduce memory and computational load.
  • Preprocessing on Edge:
    • Signal filtering (e.g., noise reduction in biomedical signals).
    • Feature extraction (e.g., time-series features for motion classification).

Model Selection & Optimization

  • Model Selection:
    • Lightweight CNNs (for image processing).
    • Recurrent Neural Networks (RNNs) / LSTMs (for time-series data like ECG).
    • TinyML models optimized for microcontrollers (e.g., TensorFlow Lite, PyTorch Mobile).
  • Model Optimization for Edge Deployment:
    • Quantization: Convert floating-point models to int8 or int16 to reduce size and computation load.
    • Pruning: Remove unnecessary neurons or layers while preserving accuracy.
    • Distillation: Train a smaller model using knowledge from a larger one.

Hardware Selection

  • Processing Unit:
    • Microcontrollers (MCUs) (e.g., ARM Cortex-M, ESP32) → Low-power, simple AI tasks.
    • Edge AI Accelerators (e.g., Google Edge TPU, NVIDIA Jetson Nano) → More complex AI processing.
    • FPGAs (Field-Programmable Gate Arrays) → Custom AI workloads for high-speed processing.
  • Memory & Storage:
    • RAM Optimization: Choose embedded SRAM or external DRAM depending on model size.
    • Flash Storage: Store inference models efficiently.
  • Connectivity:
    • Offline processing for low-latency applications.
    • Edge-to-cloud integration for periodic updates.

Deployment & Model Inference

  • Convert trained AI models into optimized edge-compatible formats (e.g., TensorFlow Lite, ONNX).
  • Implement real-time inference using hardware-accelerated libraries (e.g., TensorRT, OpenVINO).
  • Optimize firmware for energy efficiency using duty-cycling techniques (process only when necessary).

Testing, Validation, and Continuous Improvement

  • Edge Benchmarking:
    • Measure inference speed and power consumption.
    • Validate model accuracy on real-world edge-generated data.
  • Security & Reliability:
    • Implement secure boot & firmware updates to prevent cyber threats.
    • Ensure robust error handling for sensor malfunctions.
  • Feedback & Model Updating:
    • If connected to a cloud system, update models periodically using federated learning.
    • Optimize AI pipelines with incremental learning on-device where feasible.

Final Deployment & Scaling

  • Deploy at scale, ensuring the Edge AI model adapts to different environments.
  • Implement remote monitoring & diagnostics for predictive maintenance.
  • Enable over-the-air (OTA) updates to improve AI models post-deployment.

Abstract

The hardware-software co-design approach is the most widely used methodology for Edge AI device development. It ensures:

  • Real-time performance with optimized AI models.
  • Energy-efficient processing for battery-operated or low-power devices.
  • Scalability and security in edge environments.

This methodology is industry-standard and used by leading companies in healthcare, automotive, and industrial IoT, ensuring robust and reliable Edge AI solutions.

Example of application

Use case

A wearable ECG monitoring device designed for continuous heart health tracking and arrhythmia detection. This Edge AI-based solution analyzes ECG signals in real-time on a low-power microcontroller, providing instant alerts for cardiac irregularities without relying on cloud computing.

Step 1: Problem Definition & Use Case Analysis

Objective

Detect abnormal heart rhythms (arrhythmias) in real-time using a wearable ECG device.

Operational Constraints:

  • Must be energy-efficient (battery-operated, low power consumption).
  • Needs real-time inference for immediate alerts.
  • Should operate offline, but sync with mobile apps for periodic review.

Key Challenges:

  • Processing ECG data on a low-power Edge device.
  • Minimizing false positives/negatives in arrhythmia detection.
  • Ensuring high reliability and accuracy.

Step 2: Data Collection & Preprocessing

Sensor Selection:

  • ECG sensor (e.g., AD8232) captures raw heart signals.
  • Accelerometer (optional) for motion artifacts reduction.

Edge-Compatible Data Acquisition:

  • Sample rate: 250 Hz (sufficient for arrhythmia detection).
  • Use on-device filtering (low-pass filters) to remove noise.

Preprocessing on Edge:

  • Apply Butterworth filters for noise reduction.
  • R-peak detection using Pan-Tompkins algorithm for heart rate calculation.
  • Extract features like RR intervals, QRS width, and HR variability.

Step 3: Model Selection & Optimization

AI Model:

  • Use 1D CNN + LSTM hybrid model (efficient for ECG signal processing).
  • Train the model using MIT-BIH Arrhythmia Database.

Model Optimization for Edge AI:

  • Quantization: Convert model to int8 precision using TensorFlow Lite.
  • Pruning: Remove redundant neurons to reduce computation load.
  • Knowledge Distillation: Train a smaller model from a high-performing one.

Step 4: Hardware Selection

Microcontroller (MCU):

  • Nordic nRF52840 (low-power ARM Cortex-M4 + BLE connectivity).
  • Alternative: ESP32 (for low-cost AI inference).

Memory & Storage:

  • RAM: 512KB (optimized for Edge AI processing).
  • Flash storage: 4MB (stores ECG data logs for later analysis).

Connectivity:

  • Bluetooth Low Energy (BLE) for periodic sync with mobile apps.
  • Can function offline with real-time alerts.

Step 5: Deployment & Model Inference

  • Convert trained TensorFlow model → TensorFlow Lite for Edge AI inference.
  • Deploy on the Nordic nRF52840 MCU using TensorFlow Lite for Microcontrollers.
  • Use hardware-accelerated inference for efficient processing.
  • Implement event-driven processing (AI runs only on abnormal detections to save power).

Step 6: Testing, Validation, and Continuous Improvement

Edge Benchmarking:

  • Real-time inference latency: <10 ms per ECG segment.
  • Power consumption: 5mW (optimized for long battery life).

Security & Reliability:

  • Secure Boot & Firmware Updates to prevent hacking.
  • Adaptive AI Models: Learns individual patient heart patterns to reduce false alarms.

Feedback & Model Updating:

  • Sync detected arrhythmia events with a cloud server for validation.
  • Use federated learning to improve AI models without sharing raw patient data.

Step 7: Final Deployment & Scaling

  • Mass production of the device for hospitals, clinics, and home use.
  • Integration with mobile apps for patient-doctor communication.
  • Regulatory Approval: Submit for FDA/CE certification for medical device compliance.
  • Over-the-Air (OTA) Updates: Allow model updates based on new ECG patterns.